A Systematic Review of the Relative Frequency and Risk Factors for Prolonged Opioid Prescription Following Surgery and Trauma Among Adults
Bibliographic record
Abstract
In Brief Objective: The aim of this study was to examine the relative frequency of and risk factors for prolonged opioid prescription (Rx_3–6: ≥1 opioid prescription of any length between 3 and 6 months postevent) and long-term opioid prescription (Rx_>6: ≥1 opioid prescription of any length >6 months postevent) after surgery/trauma. Summary Background Data: Eighty percent of patients undergoing surgery are prescribed opioids; for many this initial time-limited therapy continues for months after surgery. Methods: Included studies were published between January 1998 and April 2018, examined opioid use ≥3 months after surgery/trauma requiring hospitalization, and considered pre-event opioid prescription status. Empirical studies were identified via a systematic literature search. Two independent reviewers assessed studies for inclusion and conducted data extraction and quality appraisal. Results: Thirty-five of the 10,003 screened articles were included; most were retrospective studies of medicoadministrative databases; all studies were observational. The median relative frequency of Rx_3–6 and Rx_>6 was 4.1% and 2.6%, respectively, among patients with no/short-term opioid prescription pre-event and 50.9% and 58.5%, respectively, among patients with prolonged opioid prescription pre-event. Income levels, tobacco dependence, use of antidepressants, and pre-event opioid prescriptions are associated with increased risk of Rx_3–6/Rx_>6. The use of benzodiazepines (current use) or muscle relaxants and the presence of alcohol/drug dependence were found to be potential risk factors for Rx_3–6/Rx_>6 among patients with no/short-term opioid prescription pre-event. Conclusions: Identified risk factors for Rx_3–6/Rx_>6 were predominantly psychosocial factors. This points to the importance of assessing mental and social health before surgery and acutely during hospitalization to ensure safe and optimal recovery.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".